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首页> 外文会议>2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, 2015 IEEE 12th International Conference on Embedded Software and Systems >Application Modeling for Scalable Simulation of Massively Parallel Systems
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Application Modeling for Scalable Simulation of Massively Parallel Systems

机译：大规模并行系统可扩展仿真的应用建模

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Macro-scale simulation has been advanced as one tool for application -- architecture co-design to express operation of exascale systems. These simulations approximate the behavior of system components, trading off accuracy for increased evaluation speed. Application skeletons serve as the vehicle for these simulations, but they require accurately capturing the execution behavior of computation. The complexity of application codes, the heterogeneity of the platforms, and the increasing importance of simulating multiple performance metrics (e.g., execution time, energy) require new modeling techniques. We propose flexible statistical models to increase the fidelity of application simulation at scale. We present performance model validation for several exascale mini-applications that leverage a variety of parallel programming frameworks targeting heterogeneous architectures for both time and energy performance metrics. When paired with these statistical models, application skeletons were simulated on average 12.5 times faster than the original application incurring only 6.08% error, which is 12.5% faster and 33.7% more accurate than baseline models.
机译：宏观仿真已作为一种应用程序进行了高级开发-架构协同设计以表示亿亿级系统的操作。这些仿真近似了系统组件的行为，为提高评估速度而牺牲了准确性。应用程序框架充当这些仿真的载体，但是它们需要准确地捕获计算的执行行为。应用程序代码的复杂性，平台的异构性以及模拟多个性能指标（例如，执行时间，能源）的重要性日益提高，因此需要新的建模技术。我们提出了灵活的统计模型，以提高大规模进行应用程序仿真的保真度。我们介绍了几种百亿亿次微型应用程序的性能模型验证，这些应用程序利用针对异类架构的各种并行编程框架来获得时间和能源性能指标。与这些统计模型配对时，模拟应用程序框架的速度平均比原始应用程序快12.5倍，仅产生6.08％的错误，这比基线模型快了12.5％，准确度提高了33.7％。

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